Spaces:
Runtime error
Runtime error
File size: 8,052 Bytes
d945eeb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
from typing import Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from jaxtyping import Float, Integer
from torch import Tensor
from .mesh import Mesh
class IsosurfaceHelper(nn.Module):
points_range: Tuple[float, float] = (0, 1)
@property
def grid_vertices(self) -> Float[Tensor, "N 3"]:
raise NotImplementedError
@property
def requires_instance_per_batch(self) -> bool:
return False
class MarchingTetrahedraHelper(IsosurfaceHelper):
def __init__(self, resolution: int, tets_path: str):
super().__init__()
self.resolution = resolution
self.tets_path = tets_path
self.triangle_table: Float[Tensor, "..."]
self.register_buffer(
"triangle_table",
torch.as_tensor(
[
[-1, -1, -1, -1, -1, -1],
[1, 0, 2, -1, -1, -1],
[4, 0, 3, -1, -1, -1],
[1, 4, 2, 1, 3, 4],
[3, 1, 5, -1, -1, -1],
[2, 3, 0, 2, 5, 3],
[1, 4, 0, 1, 5, 4],
[4, 2, 5, -1, -1, -1],
[4, 5, 2, -1, -1, -1],
[4, 1, 0, 4, 5, 1],
[3, 2, 0, 3, 5, 2],
[1, 3, 5, -1, -1, -1],
[4, 1, 2, 4, 3, 1],
[3, 0, 4, -1, -1, -1],
[2, 0, 1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1],
],
dtype=torch.long,
),
persistent=False,
)
self.num_triangles_table: Integer[Tensor, "..."]
self.register_buffer(
"num_triangles_table",
torch.as_tensor(
[0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long
),
persistent=False,
)
self.base_tet_edges: Integer[Tensor, "..."]
self.register_buffer(
"base_tet_edges",
torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),
persistent=False,
)
tets = np.load(self.tets_path)
self._grid_vertices: Float[Tensor, "..."]
self.register_buffer(
"_grid_vertices",
torch.from_numpy(tets["vertices"]).float(),
persistent=False,
)
self.indices: Integer[Tensor, "..."]
self.register_buffer(
"indices", torch.from_numpy(tets["indices"]).long(), persistent=False
)
self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None
center_indices, boundary_indices = self.get_center_boundary_index(
self._grid_vertices
)
self.center_indices: Integer[Tensor, "..."]
self.register_buffer("center_indices", center_indices, persistent=False)
self.boundary_indices: Integer[Tensor, "..."]
self.register_buffer("boundary_indices", boundary_indices, persistent=False)
def get_center_boundary_index(self, verts):
magn = torch.sum(verts**2, dim=-1)
center_idx = torch.argmin(magn)
boundary_neg = verts == verts.max()
boundary_pos = verts == verts.min()
boundary = torch.bitwise_or(boundary_pos, boundary_neg)
boundary = torch.sum(boundary.float(), dim=-1)
boundary_idx = torch.nonzero(boundary)
return center_idx, boundary_idx.squeeze(dim=-1)
def normalize_grid_deformation(
self, grid_vertex_offsets: Float[Tensor, "Nv 3"]
) -> Float[Tensor, "Nv 3"]:
return (
(self.points_range[1] - self.points_range[0])
/ self.resolution # half tet size is approximately 1 / self.resolution
* torch.tanh(grid_vertex_offsets)
) # FIXME: hard-coded activation
@property
def grid_vertices(self) -> Float[Tensor, "Nv 3"]:
return self._grid_vertices
@property
def all_edges(self) -> Integer[Tensor, "Ne 2"]:
if self._all_edges is None:
# compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)
edges = torch.tensor(
[0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],
dtype=torch.long,
device=self.indices.device,
)
_all_edges = self.indices[:, edges].reshape(-1, 2)
_all_edges_sorted = torch.sort(_all_edges, dim=1)[0]
_all_edges = torch.unique(_all_edges_sorted, dim=0)
self._all_edges = _all_edges
return self._all_edges
def sort_edges(self, edges_ex2):
with torch.no_grad():
order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
order = order.unsqueeze(dim=1)
a = torch.gather(input=edges_ex2, index=order, dim=1)
b = torch.gather(input=edges_ex2, index=1 - order, dim=1)
return torch.stack([a, b], -1)
def _forward(self, pos_nx3, sdf_n, tet_fx4):
with torch.no_grad():
occ_n = sdf_n > 0
occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
occ_sum = torch.sum(occ_fx4, -1)
valid_tets = (occ_sum > 0) & (occ_sum < 4)
occ_sum = occ_sum[valid_tets]
# find all vertices
all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)
all_edges = self.sort_edges(all_edges)
unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
unique_edges = unique_edges.long()
mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
mapping = (
torch.ones(
(unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device
)
* -1
)
mapping[mask_edges] = torch.arange(
mask_edges.sum(), dtype=torch.long, device=pos_nx3.device
)
idx_map = mapping[idx_map] # map edges to verts
interp_v = unique_edges[mask_edges]
edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
edges_to_interp_sdf[:, -1] *= -1
denominator = edges_to_interp_sdf.sum(1, keepdim=True)
edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
idx_map = idx_map.reshape(-1, 6)
v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))
tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
num_triangles = self.num_triangles_table[tetindex]
# Generate triangle indices
faces = torch.cat(
(
torch.gather(
input=idx_map[num_triangles == 1],
dim=1,
index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],
).reshape(-1, 3),
torch.gather(
input=idx_map[num_triangles == 2],
dim=1,
index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],
).reshape(-1, 3),
),
dim=0,
)
return verts, faces
def forward(
self,
level: Float[Tensor, "N3 1"],
deformation: Optional[Float[Tensor, "N3 3"]] = None,
) -> Mesh:
if deformation is not None:
grid_vertices = self.grid_vertices + self.normalize_grid_deformation(
deformation
)
else:
grid_vertices = self.grid_vertices
v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)
mesh = Mesh(
v_pos=v_pos,
t_pos_idx=t_pos_idx,
# extras
grid_vertices=grid_vertices,
tet_edges=self.all_edges,
grid_level=level,
grid_deformation=deformation,
)
return mesh
|